Looking at the evidence I've already gathered, I have the distinct counts for all categorical columns in the dataset. Let me compile this into a comprehensive answer. From the inspect_columns results (step-0 and step-1), I can see the approximate distinct counts for the first 30 columns. From step-2, I have exact distinct counts for 19 key categorical columns. Let me query for the remaining categorical columns to provide a complete picture. {"action": "tool_call", "reasoning": "I have distinct counts for many columns, but the inspect_columns only showed 30 of 61 columns. I need to get the remaining categorical columns to provide a complete answer. I'll query for distinct counts of the columns not yet covered.", "tool": "query_sql", "args": {"sql": "SELECT COUNT(DISTINCT \"GPU supplier (secondary)\") as GPU_supplier_secondary_distinct, COUNT(DISTINCT \"Include in Standard Analysis\") as Include_in_Standard_Analysis_distinct, COUNT(DISTINCT \"Exclude\") as Exclude_distinct, COUNT(DISTINCT \"Noteworthy\") as Noteworthy_distinct, COUNT(DISTINCT \"Largest existing cluster when first operational\") as Largest_existing_cluster_distinct, COUNT(DISTINCT \"Decommissioned Date (if applicable)\") as Decommissioned_Date_distinct FROM raw.ai_supercomputers", "purpose": "Get distinct counts for remaining categorical columns not covered in previous queries", "source": "raw"}}